Automatic Fastening Inspection of High-strength Bolts in Steel Bridge Construction

Abstract

A large number of high-strength bolts are used to connect multiple steel members in steel bridge construction, and workers are currently visually confirming whether each bolt is fastened correctly or not. The workers have a heavy burden to confirm thousands of high-strength bolts, and there is a risk of missing inspections or personal injury accidents. Therefore, this paper proposes an automatic method to inspect whether the high-strength bolt is installed correctly or not. In the proposed method, images of multiple high-strength bolts are raster-scanned with a search window, and the histogram of oriented gradient features is extracted. After, a bolt presence probability map is generated from the high-strength bolt detection windows obtained by the support vector machine. For each detected bolt, a machine learning classifier inspects the bolt is installed correctly, installed incorrectly, or impossible to inspect due to the incorrect marking. Experiments on images acquired at the steel bridge construction sites showed that the proposed method is able to detect high-strength bolts with high accuracy, but there were some false positives in the background area. Due to the marker line detection errors and the difference in bolt acquisition angle, the fastening inspection accuracy was not high enough for practical use. In the future, we will improve the accuracy by taking into account the color, density, and thickness of markers, which can be easily extracted by general image processing techniques.